SevenNet-Omni-i12
Discovery: energy and convex hull diagnostics
Missing preds: 0
Loading formation energy parity data...
Per-element convex hull distance errors
1 H 0.13
2 He Helium
3 Li 0.02
4 Be 0.02
5 B 0.05
6 C 0.05
7 N 0.07
8 O 0.11
9 F 0.10
10 Ne Neon
11 Na 0.02
12 Mg 0.03
13 Al 0.04
14 Si 0.05
15 P 0.05
16 S 0.06
17 Cl 0.08
18 Ar Argon
19 K 0.03
20 Ca 0.03
21 Sc 0.03
22 Ti 0.04
23 V 0.06
24 Cr 0.08
25 Mn 0.11
26 Fe 0.09
27 Co 0.04
28 Ni 0.04
29 Cu 0.03
30 Zn 0.03
31 Ga 0.04
32 Ge 0.05
33 As 0.05
34 Se 0.08
35 Br 0.07
36 Kr Krypton
37 Rb 0.03
38 Sr 0.03
39 Y 0.04
40 Zr 0.04
41 Nb 0.05
42 Mo 0.05
43 Tc 0.03
44 Ru 0.05
45 Rh 0.04
46 Pd 0.04
47 Ag 0.03
48 Cd 0.03
49 In 0.05
50 Sn 0.04
51 Sb 0.05
52 Te 0.11
53 I 0.05
54 Xe 0.00
55 Cs 0.03
56 Ba 0.03
57 La 0.03
58 Ce 0.03
59 Pr 0.03
60 Nd 0.03
61 Pm 0.03
62 Sm 0.03
63 Eu 0.06
64 Gd 0.04
65 Tb 0.03
66 Dy 0.03
67 Ho 0.03
68 Er 0.03
69 Tm 0.03
70 Yb 0.04
71 Lu 0.03
72 Hf 0.04
73 Ta 0.07
74 W 0.04
75 Re 0.04
76 Os 0.05
77 Ir 0.06
78 Pt 0.05
79 Au 0.05
80 Hg 0.03
81 Tl 0.03
82 Pb 0.05
83 Bi 0.04
84 Po Polonium
85 At Astatine
86 Rn Radon
87 Fr Francium
88 Ra Radium
89 Ac 0.03
90 Th 0.04
91 Pa 0.04
92 U 0.05
93 Np 0.09
94 Pu 0.20
95 Am Americium
96 Cm Curium
97 Bk Berkelium
98 Cf Californium
99 Es Einsteinium
100 Fm Fermium
101 Md Mendelevium
102 No Nobelium
103 Lr Lawrencium
104 Rf Rutherfordium
105 Db Dubnium
106 Sg Seaborgium
107 Bh Bohrium
108 Hs Hassium
109 Mt Meitnerium
110 Ds Darmstadtium
111 Rg Roentgenium
112 Cn Copernicum
113 Nh Nihonium
114 Fl Flerovium
115 Mc Moscovium
116 Lv Livermorium
117 Ts Tennessine
118 Og Oganesson
57-71 La-Lu Lanthanides
89-103 Ac-Lr Actinides
ML vs DFT Lattice Thermal Conductivity
Loading κ parity data...
Trained By
Model Info
- Version v0.12.0
- Role Interatomic potential
- Architecture gnn
- Targets EFSG
- Openness OSOD
- Discovery Train Task S2EFS
- Discovery Test Task IS2RE-SR
Training Set
COSMOSDataset: 243M structures
description
SevenNet is a graph neural network interatomic potential package that supports parallel molecular dynamics simulations. The SevenNet-Omni model employs a multi-task training strategy that jointly optimizes universal and task-specific parameters via selective regularization and domain-bridging strategies, enabling robust transferability across molecules, bulk crystals, and surfaces. Trained on 15 open datasets spanning molecular, inorganic, and interfacial chemistries, SevenNet-Omni achieves state-of-the-art cross-domain accuracy while maintaining high in-domain fidelity.
Hyperparams
- evaluation:
{"max_force":0.02,"max_steps":800,"ase_optimizer":"FIRE","cell_filter":"FrechetCellFilter","kappa":{"protocol":"phonondb-v1","displacement_distance":0.03,"save_forces":true}} - architecture:
{"n_layers":12,"graph_construction_radius":6} - training:
{"batch_size":256,"initial_learning_rate":0.0001,"epochs":2,"optimizer":"Adam"} - upstream_config:
{"loss":"MAE/L2MAE/L2MAE","loss_weights":{"energy":1,"force":1,"stress":0.0005},"learning_rate_schedule":"onecyclelr - max_lr=0.0001, pct_start=0.05, anneal_strategy=cos, div_factor=25, final_div_factor=1e4","n_features":["128x0e","128x0e+64x1o+32x2e+32x3o","128x0e+64x1o+32x2e+32x3o","128x0e+64x1o+32x2e+32x3o","128x0e+64x1o+32x2e+32x3o","128x0e+64x1o+32x2e+32x3o","128x0e+64x1o+32x2e+32x3o","128x0e+64x1o+32x2e+32x3o","128x0e+64x1o+32x2e+32x3o","128x0e+64x1o+32x2e+32x3o","128x0e+64x1o+32x2e+32x3o","128x0e+64x1o+32x2e+32x3o","128x0e"],"n_radial_bessel_basis":8,"sph_harmonics_l_max":3}
Dependencies
- sevenn
- torch ==2.7.0
- torch-geometric ==2.6.1
- ase ==3.23.0
- pymatgen ==2025.10.7
- numpy ==1.26.4